• Energy Research
• economics and business close
• PL close
• Aurora Universities Network close

Abstract The inverse relationship between urban density and greenhouse gas (GHG) emissions caused by driving is well established. However, at the same time the few existing studies have observed higher levels of long-distance travel and particularly air travel in the same densely built parts of urban regions. This may lead to GHG emissions reduction in local travel offset by the concomitant increase in long-distance travel. With this study we aim to identify the main factors involved in differences in local, national and long-distance travel patterns and the resulting GHG emissions, with a special focus on the role of the different urban zones in the Helsinki Metropolitan Area (HMA) in Finland. We used a softGIS survey to collect data on the personal travel of young adults living in HMA. SoftGIS methodology provides the opportunity to obtain detailed spatial data on participants' residential locations, travel destinations, and destination characteristics such as travel modes, frequencies and trip purposes. Special attention was paid to national and international trips, for which data were collected over 12 months, a period long enough to capture actual travel patterns. GHG emissions were assessed with a wide scope life cycle assessment (LCA) approach, including vehicles and infrastructure, and the results were elaborated with a two-part regression model on participation in travel and amount of GHG emissions. The study found that the residential location was associated with travel emissions on all scales, and independently from major socioeconomic characteristics. Residents of centrally located and densely built urban zones have on average lower emissions from local travel but higher emissions from international travel than residents of car-oriented suburban zones, and the association holds true after controlling for income, education level and household type. Differences in emissions from local travel between most central and most suburban zones were almost completely offset by differences in emissions from international travel. International long-distance trips were a dominant source of travel-related GHG emissions in all urban zones, particularly due to plane flights.

Little is known about foreign service spouses’ resilience and experience with stress, or which organizational supports are most effective for them in a crisis. The aims of this study were therefore to (1) measure resilience and personal burnout among foreign service spouses during the COVID-19 pandemic; (2) test whether resilience was associated with personal burnout; and (3) investigate which organizational supports from Ministries of Foreign Affairs (MFAs), if any, were associated with resilience or personal burnout. A total of 421 foreign service spouses (316 women and 105 men, 89% from European MFAs) completed an online survey; data were analyzed using multiple regression analyses. Nearly a third of respondents (31%) had moderate personal burnout and 4.5% had high or severe personal burnout. Higher resilience was significantly correlated with lower personal burnout scores. While knowledge that they would be evacuated if necessary was significantly correlated with greater resilience, only 27.3% of respondents knew of this support. A designated contact person at the MFA, and knowledge of a policy abroad to reduce Covid-related risks were associated with lower personal burnout scores. This study provides a baseline for resilience and personal burnout in this population, and suggests which organizational supports may be most effective during a crisis.

Negative relationships between urban density and greenhouse gas emissions from daily travel are well established in the literature. However, recent research suggests that higher urban density is associated with higher emissions from long-distance leisure travel, such as car weekend trips and international flights. This article presents the first systematic review of empirical evidence on these associations and discusses potential explanations. A two-step article selection process yielded 27 empirical articles, complemented by one article published during the review process. When international travel is included in the analysis, the results suggest that residents of the largest cities, and particularly those from centrally located and densely built areas, travel more to cover long distances than do others, after controlling for demographic and socioeconomic variables. When only domestic travel is included, residents of larger settlements and areas of higher density engage in less long-distance travel for leisure purposes than those living in smaller settlements and sparsely built areas. The results of the review are indicative and warrant more research. Generalization is currently limited because of the wide variety of travel behavior measures used, consideration of different travel modes and trip purposes, and geographic scope. There is a strong need for replication of the results using consistent methodology, using data from longer and more recent time spans, and expanding to more diverse geographical settings, especially outside Europe. The systematic review is followed by a narrative review of theoretical explanations of the associations. The most common explanations include: rebound effects, the compensation hypothesis, access to transport infrastructure, urban lifestyles, sociopsychological characteristics, and social networks. Socioeconomic variables are controlled in a majority of the reviewed studies, and business travel is excluded from the review, so the concentration of wealth and business in cities may explain the findings only to some extent. Nonetheless, there is not enough empirical evidence on the causal character of the associations and therefore further qualitative and multidisciplinary work is needed. Compact city and urban densification policies are not strongly challenged by current evidence, and most common policy recommendations point to including air travel into carbon taxing or quota schemes.

This article presents findings from a mixed-methods study on residential location and travel in the Reykjavik capital region, Iceland, drawing on a combination of a tailor-made questionnaire survey and in-depth qualitative interviews, including cross-sectional and before–after analyses. A residential location close to the main city center of Reykjavik contributes to shorter travel distances and lower shares of car travel. The effect of proximity to the city center is particularly strong for commuting but exists for non-work travel and overall car-driving distances too. There are also effects of proximity to a main second-order center and local centers and of local-area population density, but these effects apply to fewer aspects of travel. The rationales for location of activities and travel mode choice identified in the qualitative interviews explain why travel distances and modes tend to depend more on proximity to the main city center than on neighborhood-scale built environment characteristics. The main patterns found in the Reykjavik area are in line with findings in several earlier studies in the Nordic countries and elsewhere. However, through its methodological approach, the investigation adds to the few studies on the topic where results are underpinned by combined qualitative and quantitative methods and inclusion of before–after analyses.

In this paper, we analyze the influence of forward trading on the volatility of spot power prices, in models where forward contracts are strategic tools used by energy producers to obtain profit security. We define volatility as the variance of the percentage change in spot power prices over a given time interval. As shown in Sapio (2008), volatility is related to stochastic fluctuations in preference and technology fundamentals, and is tuned by the price-elasticity of demand and supply, evaluated at equilibrium. We study two cases. First, we analyze the volatility implications of a model wherein the amount of forward trading is fixed, and producers compete a la Cournot. Fixed forward trading increases spot volatility, because forwards lower the spot price level, corresponding to a less elastic region of a linear demand function. However, if the amount of forward trading is endogenous, as in the two-stage model of Allaz (1992), producers can anticipate the spot market impact of stochastic shocks on fundamentals and ldquosterilizerdquo them. As a result, spot price volatility is closer to the value implied by an efficient market. Our theoretical results are illustrated by means of a simple simulation study.

We examine the novel problem of the estimation of transaction arrival processes in the intraday electricity markets. We model the inter-arrivals using multiple time-varying parametric densities based on the generalized F distribution estimated by maximum likelihood. We analyse both the in-sample characteristics and the probabilistic forecasting performance. In a rolling window forecasting study, we simulate many trajectories to evaluate the forecasts and gain significant insights into the model fit. The prediction accuracy is evaluated by a functional version of the MAE (mean absolute error), RMSE (root mean squared error) and CRPS (continuous ranked probability score) for the simulated count processes. This paper fills the gap in the literature regarding the intensity estimation of transaction arrivals and is a major contribution to the topic, yet leaves much of the field for further development. The study presented in this paper is conducted based on the German Intraday Continuous electricity market data, but this method can be easily applied to any other continuous intraday electricity market. For the German market, a specific generalized gamma distribution setup explains the overall behaviour significantly best, especially as the tail behaviour of the process is well covered.

Mobile financial services such as M-PESA in Kenya are said to promote inclusion. Yet only 7.6 per cent of the Kenyans in the 2013 Financial Inclusion Insights dataset have ever used an M-PESA account to save for a future purchase. This paper uses a novel, three-step probit analysis to identify the socio-demographic characteristics of, successively, respondents who do not have access to a SIM card, have access to a SIM but do not have an M-PESA account, and, finally, have an account but do not save on it. We find that those who are excluded in the early stages are predominantly poor, non-educated, and female. For the final stage, we find that those who are in a position to save on their phone—the phone owners, the better educated—are less likely to do so. These results go against the traditional optimistic discourse on mobile savings as a prime path to financial inclusion. As such, our findings corroborate qualitative research that indicates that Kenyans have other needs, and want their money to circulate and ‘work’.

We present a novel approach to probabilistic electricity price forecasting which utilizes distributional neural networks. The model structure is based on a deep neural network that contains a so-called probability layer. The network's output is a parametric distribution with 2 (normal) or 4 (Johnson's SU) parameters. In a forecasting study involving day-ahead electricity prices in the German market, our approach significantly outperforms state-of-the-art benchmarks, including LASSO-estimated regressions and deep neural networks combined with Quantile Regression Averaging. The obtained results not only emphasize the importance of higher moments when modeling volatile electricity prices, but also -- given that probabilistic forecasting is the essence of risk management -- provide important implications for managing portfolios in the power sector.